Design and build scalable ETL/ELT pipelines using both batch and streaming approaches.
Develop ingestion workflows from multiple sources such as databases, APIs, and event streams.
Implement ingestion strategies including full load, incremental load, and CDC.
Orchestrate data workflows using Apache Airflow.
Manage data connectors using Airbyte.
Work with Databricks Lakehouse to build and optimize data processing pipelines.
Write and optimize complex SQL queries for analytics and transformation.
Build modular and testable data models using dbt (staging → intermediate → marts).
Maintain data quality, observability, and reliability across the platform.
Work with AWS services such as S3, Lambda, EC2, IAM.
Containerize data services using Docker and Kubernetes (EKS) when needed.
Document pipelines, data models, and data dictionaries for long-term maintainability.

At least 5 years of experience in Data Engineering.
Strong understanding of data architectures such as Data Lake, Data Warehouse, and Lakehouse.
Hands-on experience with ETL/ELT pipelines, including batch and streaming processing.
Familiar with ingestion patterns: full load, incremental, CDC, event-driven.
Experience working with Databricks (Delta Live Tables, Jobs, Notebooks).
Strong skills in PySpark or Spark SQL for large-scale data processing.
Solid understanding of Delta Lake (ACID, time travel, schema evolution).
Experience with Apache Airflow (DAGs, scheduling, monitoring).
Experience with Airbyte or similar ingestion tools.
Strong SQL skills (CTEs, joins, window functions, query optimization).
Experience with dbt for transformation, testing, and documentation.
Hands-on experience with AWS (S3, Lambda, IAM, etc.).

Nice to have

Data Engineer

Key skills